Quantitative analysis of hillshed geomorphology and critical zone function: Raising the hillshed to watershed status

Landscapes are frequently delineated by nested watersheds and river networks ranked via stream orders. Landscapes have only recently been delineated by their interfluves and ridge networks, and ordered based on their ridge connectivity. There are, however, few studies that have quantitatively investigated the connections between interfluve networks and landscape morphology and environmental processes. Here, we ordered hillsheds using methods complementary to traditional watersheds, via a hierarchical ordering of interfluves, and we defined hillsheds to be landscape surfaces from which soil is shed by soil creep or any type of hillslope transport. With this approach, we demonstrated that hillsheds are most useful for analyses of landscape structure and processes. We ordered interfluve networks at the Calhoun Critical Zone Observatory (CZO), a North American Piedmont landscape, and demonstrated how interfluve networks and associated hillsheds are related to landscape geomorphology and processes of land management and land-use history, accelerated agricultural gully erosion, and bedrock weathering depth (i.e., regolith depth). Interfluve networks were ordered with an approach directly analogous to that first proposed for ordering streams and rivers by Robert Horton in the GSA Bulletin in 1945. At the Calhoun CZO, low-order hillsheds are numerous and dominate most of the observatory’s ∼190 km2 area. Low-order hillsheds are relatively narrow with small individual areas, they have relatively steep slopes with high curvature, and they are relatively low in elevation. In contrast, high-order hillsheds are few, large in individual area, and relatively level at high elevation. Cultivation was historically abandoned by farmers on severely eroding low-order hillsheds, and in fact agriculture continues today only on high-order hillsheds. Low-order hillsheds have an order of magnitude greater intensity of gullying across the Calhoun CZO landscape than high-order hillsheds. In addition, although modeled regolith depth appears to be similar across hillshed orders on average, both maximum modeled regolith depth and spatial depth variability decrease as hillshed order increases. Land management, geomorphology, pedology, and studies of land-use change can benefit from this new approach pairing landscape structure and analyses.

Modeling 13 Archimedean solids by and object-oriented language

The computational study of structures with chemical relevance is preceded by its modeling in such manner that no calculations can be submitted without the knowledge of their spatial atomic arrangement. In this regard, the use of an object-oriented language can be helpful both to generate the Cartesian coordinates (.xyz file format) and to obtain a ray-traced image. The modeling of chemical structures based on programming has some advantages with respect to other known strategies. The more important advantage is the generation of Cartesian coordinates that can be visualized easily by using free of charge software. Our approach facilitates the spatial vision of complex structures and make tangible the chemistry concepts delivered in the classroom. In this article an undergraduate project is described in which students generate the Cartesian coordinates of 13 Archimedean solids based on a geometrical/programming approach. Students were guided along the project and meetings were held to integrate their ideas in a few lines of programmed codes. They improved their decision-making process and their organization and collecting information capabilities, as much as their reasoning and spatial depth. The final products of this project are the coded algorithms and those made tangible the grade of learning/understanding derived of this activity.

Dronar—Geoforensic Search Sonar from a Drone

Lightweight sonar devices may be tethered to an unmanned aerial vehicle or drone and quickly deployed over water for real-time imaging in 2D and the on site creation of geolocated, interactive bathymetric maps without the need for a boat. We show how such data is useful in the preliminary stages of water searches, by providing geophysicists, hydrologists and divers with spatial depth information, the distribution of dive and equipment hazards such as entanglement objects (weed, discarded items) and sediment types. One bathymetry case study location is described in detail, with a further two summarized to demonstrate reconnaissance surveys. Limitations of drone-based sonar surveys are outlined, including dense water weed cover; limits on flight times and adverse weather conditions.

Human Motion Recognition Based on Multimodal Characteristics of Learning Quality in Football Scene

The task of human motion recognition based on video is widely concerned, and its research results have been widely used in intelligent human-computer interaction, virtual reality, intelligent monitoring, security, multimedia content analysis, etc. The purpose of this study is to explore the human action recognition in the football scene combined with learning quality related multimodal features. The method used in this study is to select BN-Inception as the underlying feature extraction network and use uncontrolled environment and real world to capture datasets UCFl01 and HMDB51, and pretraining is carried out on the ImageNet dataset. The spatial depth convolution network takes image frame as input, and the temporal depth convolution network takes stacked optical flow as input to carry out human action multimodal identification. In the results of multimodal feature fusion, the accuracy of UCFl01 dataset is generally high, all of which are over 80%, and the highest is 95.2%, while the accuracy of HMDB51 dataset is about 70%, and the lowest is only 56.3%. It can be concluded that the method of this study has higher accuracy and better effect in multimodal feature acquisition, and the accuracy of single-mode feature recognition is significantly lower than that of multimodal feature recognition. It provides an effective method for the multimodal feature of human motion recognition in the scene of football or sports.

Depth of Space and Radius of the Electron

The cosmological model of the expanding balloon in 4D-space (CM) delivers in interaction with a homogeneous vector field exactly Newton’s law of gravitation with its 1/r-shape of the gravitational funnel. So far, the depth of space, W, in the 4-th spatial dimension can only be calculated using the theoretical approach of Feynman’s radius of excess rex=a/3 with Schwarzschild-radius a. With this, the connection to the general theory of relativity (GR) is established, but the situation is unsatisfactory. In the present study, the possibilities of an experimental approach to the calculation of spatial depth, W, are explored. The only experimental approach so far is the bending of light on a central mass. We hypothesize in addition to the main effect φ = -4a/y, i.e., the angle of diffraction of a light beam on a heavy central mass in the distance y and with Schwarzschild-radius a, an additional effect close to the center of the form φC ~ -1/y4. This additional effect has on the edge of the central mass about 1/3 of the strength of the main effect. However, its influence disappears very quickly with increasing distance. For this reason the sun cannot be used as the central mass. The bright corona and the strong magnetosphere do not allow measurements close to the sun. However, ESA’s GAIA mission puts the planet Jupiter at the center of interest. This spacecraft measures with extremely high precision the positions of billions of stars. Results of first data analyses have already been published. As a side effect - the application of the CM to small particles provides an indication that the radius of the electron could be in the order of 10-23 m.

Depth classification based on affine-invariant, weighted and kernel-based spatial depth functions

Several multivariate depth functions have been proposed in the literature, of which some satisfy all the conditions for statistical depth functions while some do not. Spatial depth is known to be invariant to spherical and shift transformations. In this paper, the possibility of using different versions of spatial depth in classification is considered. The covariance-adjusted, weighted, and kernel-based versions of spatial depth functions are presented to classify multivariate outcomes. We extend the maximal depth classification notions for the covariance-adjusted, weighted, and kernel-based spatial depth versions. The classifiers' performance is considered and compared with some existing classification methods using simulated and real datasets.

The Paramaters of Visual Art Assessment as a Measurement Instrument for Learning Products of Art Painting, Sculpture, Ceramic and Batik

Assessing learning products of visual art was unlike evaluating other learning products. Due to its relation to visual outcomes, the first assessment required proper sensitiveness and measurable references. The research was intended to be the effort to acquire parameters of visual art assessment, especially of art painting, sculpture, ceramic, and batik assessments. The findings could be used as instruments in various visual art learning activities. Data were collected by distributing a list, interviewing the informants, and having Focus Group Discussion. The respondents were visual art lectures in the Faculty of Arts and Literature in one of the state universities in norther island of Sumatera, Indonesia. They were excellent at visual art and equipped with required sensitiveness. Besides, they also had relevant education with art painting, sculpture, ceramic, and batik. The findings, in the form of assessment parameters, were first, parameters of visual art assessment were form similarity, proportion, spatial depth, technique, composition, content, ambiance, brightness, color harmony, and texture. Secondly, parameters of non-figurative sculpture assessment were technique mastery, proportion, smoothness, expression, volume, space, and idea authenticity. Thirdly, parameters of figurative sculpture assessment were visual anatomy, proportion, motion effect, and drapery. Fourthly, parameters of ceramic assessment were technique mastery/finishing, innovation creativity, novelty, smoothness, expression, harmony, and need (market need). Last, parameters of batik assessment were technique, authenticity, modernity, color harmony, innovativeness, need finishing, and affordability.

A Neurally Inspired Model of Figure Ground Organization with Local and Global Cues

Figure Ground Organization (FGO)-inferring spatial depth ordering of objects in a visual scene-involves determining which side of an occlusion boundary is figure (closer to the observer) and which is ground (further away from the observer). A combination of global cues, like convexity, and local cues, like T-junctions are involved in this process. A biologically motivated, feed forward computational model of FGO incorporating convexity, surroundedness, parallelism as global cues and spectral anisotropy (SA), T-junctions as local cues is presented. While SA is computed in a biologically plausible manner, the inclusion of T-Junctions is biologically motivated. The model consists of three independent feature channels, Color, Intensity and Orientation, but SA and T-Junctions are introduced only in the Orientation channel as these properties are specific to that feature of objects. The effect of adding each local cue independently and both of them simultaneously to the model with no local cues is studied. Model performance is evaluated based on figure-ground classification accuracy (FGCA) at every border location using the BSDS 300 figure-ground dataset. Each local cue, when added alone, gives statistically significant improvement in the FGCA of the model suggesting its usefulness as an independent FGO cue. The model with both local cues achieves higher FGCA than the models with individual cues, indicating SA and T-Junctions are not mutually contradictory. Compared to the model with no local cues, the feed-forward model with both local cues achieves ≥8.78% improvement in terms of FGCA.